{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据探索分析\n",
    "字段说明--数据集共9个字段: \n",
    "pregnants：怀孕次数\n",
    "Plasma_glucose_concentration：口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度\n",
    "blood_pressure：舒张压，单位:mm Hg\n",
    "Triceps_skin_fold_thickness：三头肌皮褶厚度，单位：mm\n",
    "serum_insulin：餐后血清胰岛素，单位:mm\n",
    "BMI：体重指数（体重（公斤）/ 身高（米）^2）\n",
    "Diabetes_pedigree_function：糖尿病家系作用\n",
    "Age：年龄\n",
    "Target：标签， 0表示不发病，1表示发病"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "5          5                           116              74   \n",
       "6          3                            78              50   \n",
       "7         10                           115               0   \n",
       "8          2                           197              70   \n",
       "9          8                           125              96   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "5                            0              0  25.6   \n",
       "6                           32             88  31.0   \n",
       "7                            0              0  35.3   \n",
       "8                           45            543  30.5   \n",
       "9                            0              0   0.0   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  \n",
       "5                       0.201   30       0  \n",
       "6                       0.248   26       1  \n",
       "7                       0.134   29       0  \n",
       "8                       0.158   53       1  \n",
       "9                       0.232   54       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath='./pima-indians-diabetes.csv'\n",
    "train=pd.read_csv(dpath, encoding='UTF-8')\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"train:\", str(train.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上输出内容得出：样本点有768个，总共有9列（含目标特征Target），9列特征没有缺失值。数据都为数值型特征（int和float）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征数据信息\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、单变量分布分析\n",
    "直方图查看：\n",
    "distplot方法可以对数值型特征绘制直方图\n",
    "countplot方法可以对离散值特征绘制条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看目标特征Target的直方图分布\n",
    "y_target=plt.figure()\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel(\"Type Of Target\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上直方图看出：不发病的糖尿病患者占总体数据的三分之二左右，而发病的糖尿病患者占三分之一左右"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看怀孕次数pregnants的直方图\n",
    "x_pregnants=plt.figure()\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel(\"Number Of Pregnants \", fontsize=12)\n",
    "plt.ylabel(\"Count Of Pregnants\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上怀孕次数的直方图看出：大部分数据都集中在怀孕次数10次以内，而怀孕次数达到11次以上的数据集占比比较少，在怀孕次数13以后的数据几乎可以忽略不计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度Plasma_glucose_concentration直方图\n",
    "x_plasma_glucose_concentration=plt.figure()\n",
    "sns.distplot(train['Plasma_glucose_concentration'], bins=40 ,kde=True)\n",
    "plt.xlabel(\"Plasma_Glucose_Concentration\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从血浆葡萄糖浓度直方图看出：大部分数据集中在浓度为100至150之间。以上基本服从正态分布，在浓度为0处有低于0.002的数据，可认为是噪声点，处理该数据时，可以丢弃。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pYHJ+Jr9567CCQD4gpiAQCap9tW2Mz0knNyP+1wcG++l+MJK85qH7/rCPxvauEdcfQoaWhpgQ6Uco7Nhf1zbizwaOuWrJJHrCjme2qU+BvJ+CQKQfiXZ94GQtnJTLjMJsfr21901/EnQKApF+lNd61wdGSRCYGdcsnczr++qobDzqdzmSQBQEIv0or2llwph0xgzB9QG/XLNsEs7Bmrd0ViB/pCAQ6UMo7Hi3rn1EDitxPNPGZXPa1HyefPOQ36VIAlEQiPThUEM7XaHwiB1W4ng+umwyO4+0cLhJzUMSoSAQ6cOx6wOj5UJxtCsWTyIlydh6sNHvUiRBKAhE+lBe20ZRbgbZ6aOvq83Y7DQ+PHc8bx1sJOw0tJcoCEQ+oCcc5t26tlFzt1BfPrpsCs0dPezzznwk2BQEIr0cajhKd8iNugvF0S6aP4H0lCTePKDmIVEQiHzA3po2jNHTf6AvGanJLJqcR2llE109Yb/LEZ8pCER6Ka9tpSgvg6y00Xd9INrSknw6e8LsPNLsdyniMwWBSJTOnhAH6tpH5d1Cvc0ozCYvM1V3D4mCQCTa1gON9ITdqOw/0FuSGUum5LG7qoXWTs1nHGSj+9xX5AS9Vl43qOsDwzmkdDwtLSngpT21vHOoibNmjvO7HPGJzghEorxeXkdxfgaZacl+lzIsivIyKMrNYOuBBr9LER8pCEQ8Hd0hthxoZGYAmoWiLS3J52DDUepaO/0uRXyiIBDxvHmgka5RNP9ArJaU5GOgi8YBpmsEJ+B47cCrV0wdxkpkKLxWXkeSwfSABUFeZiozx2fz5sFGLpw3ATPzuyQZZjojEPG8vreORZPzyEgNxvWBaEtLCqhv6+Jgg0YkDSIFgQhwtCvE1oONgb1zZuGkXG9EUl00DiIFgQiw5UADXaEwHzolmEGQkZrM/OJc3q5oIhTWiKRBoyAQAV4pqyUlyThj+li/S/HNspJ82rtC7Klq8bsUGWYKAhHg5T21nDa1gJxROP9ArGZPHENWWjJv6u6hwFEQSODVt3WxrbKJ82YX+l2Kr5KTjMVT8thxuJmWjm6/y5FhpCCQwHulrBbn4NyABwFE7h7qCTue2XbE71JkGCkIJPBe3lNDbkYKi6fk+12K70oKMhmXncaTWw/5XYoMIwWBBJpzjpf31HLu7EKSk9SRysxYWpLPq3vrONLU4Xc5MkwUBBJoe2taOdzUwXmzx/tdSsJYPCUf5+DpbYf9LkWGiYJAAu2l3bUAnDtL1weOGT8mnbkTx/C0rhMERnDvlRMhcn1gRmE2JWOz/C5lyAxmroSVi4q4+/k9VLd0MGFMxhBUJYlEZwQSWG2dPbyyt44L5qhZqLfLTy3GOXi2tMrvUmQYKAgksF7eU0NXT5hLFk70u5SEM2diDjMLs3WdICAUBBJYz5ZWkZeZypkBHlaiP2bGZacW8Xp5PfVtXX6XI0NMQSCB1B0Ks35nNRfNn0BKsn4N+nLZomJCYcdz23XReLTTb4AE0sZ99TQd7eaSBUV+l5KwFk7KpWRsJmvfURCMdgoCCaRnt1eRnpLE+XN022h/zIzLFxXz6t5amto19tBopiCQwHHO8WzpEc6bPZ6sNN1BfTwrFxXRHXL8bofuHhrNYgoCM1tpZrvMrMzMbu9jfbqZPeat32Bm073l48zsBTNrNbMfxbd0kcF551ATlU0dXLJAdwsNZGlJPpPyMtS5bJQbMAjMLBm4B7gMWADcaGYLem12C9DgnJsF/AC4y1veAXwd+Nu4VSxykp7Ycoi0lCQuXajrAwMxMy5dVMRLe2po7ezxuxwZIrGcEZwJlDnnyp1zXcCjwKpe26wCHvQePw5cZGbmnGtzzv2BSCCI+K6rJ8yatyr5yPyJ5GWl+l3OiHD5qcV09YR5fme136XIEIklCCYDB6OeV3jL+tzGOdcDNAExT/5qZp8xs01mtqmmpibW3URO2O93VVPf1sW1p/f+EZb+nD61gPFj0nn6HXUuG61iCYK+xubtPbt1LNv0yzl3r3NuuXNu+fjx6u4vQ+eJLYcozEnTaKMnICnJWLmwiBd2VdPepeah0SiWIKgASqKeTwEq+9vGzFKAPKA+HgWKxEtDWxfrd1axaulkUtWJ7IRcdmoRHd1hXtylM/bRKJZ75zYCs81sBnAIuAFY3WubNcDNwGvAdcDzzrmYzwhEhkr0yJuvldfRHXJkpSXz8IYDrF4x1cfKElvvEUtD4chxW7vtCJedWuxTVTJUBgwC51yPmd0GrAOSgfudc6Vm9k1gk3NuDXAf8JCZlRE5E7jh2P5mth/IBdLM7BrgEufc9vh/KyL9c86xobyOSXkZFOdl+l3OiJOcZCyclMvzO6ro6A6RkZrsd0kSRzH1pnHOrQXW9lp2R9TjDuD6fvadfhL1icRFWU0r1S2dXHfaFL9LGbEWTspj4/4GXt5Ty0fUB2NUUUOpBMKrZXXkpKeweEqe36WMWKeMzyEvM1VDU49CCgIZ9apbOthV1cKKmWM10uhJSE4yLp4/kee2V9HVE/a7HIkj/VbIqPfa3jqSk4wVM2Lu2iL9uPzUIlo6enhlb63fpUgcKQhkVGvt7GHLgQaWTsknJ10DzJ2sc2cXkpOewjMamnpUURDIqPby7hp6Qo7zNS9xXKSnJHPR/Ak8u/0IPSE1D40WCgIZtWpaOnl9Xx1LSvIZPybd73JGjcsWFdPQ3s2GfeozOlooCGTU+smLe+kJOS6cO8HvUkaVC+aMJzM1mbUae2jUUBDIqFTd0sEvNrzL0pJ8CnU2EFeZaclcOG8C60qrCIU1gMBooCCQUemH68voDjkunKezgaGwclERta2dbNqv5qHRQEEgo87emlYefuMAN55ZwrgcnQ0MhT+ZN4H0lCTNXDZKKAhk1Lnr6Z1kpCTxxYvm+F3KqJWTnsKH547nqbcr1blsFFAQyKjyxr56nt1exWcvOEV3Cg2xT5xRQm1rF+s1sf2IpyCQUSMcdnx77Q4m5qbzF+fN9LucUe/82eMpys3g0Y0HB95YEpq6Wsqo8T9bKnjrYCPfu34JmWkDD5Pce8x9GVjvYza/OJff76qmoqGdKQVZPlUlJ0tnBDIqNHd0c9czu1g2NZ+PLdN8xMNl+fQCAH61qcLnSuRk6IxARoXP/2ILda2dfHz5FDVVDKOCrDRmTcjhvzcd5IsXzSY5qa/pyyXR6YxARryy6hZe3VvL6dMK1DzhgzOmj+VwUwfrSnUr6UilIJARzTnHN36znbSUJC5ZWOR3OYG0YFIuMwqz+fHvy9BU5SOTgmAAnT0h1pUe4a8f28r3n9vN+p1VdPaE/C5LPM9ur+LlPbVcPH+ihpn2SZIZt54/k22Hmnlpj+YpGIkUBMfR3tXD6p9u4NaHNvP8zmqy05NZv6Oa7z27m9LKJr/LC7yO7hDfemo7cybmaNIZn330tMkU5Wbw4xfK/C5FBkFB0I/uUJjP/3ILbx5o4LvXLWbT/7uYW88/hc9dcAr5mak8tvEgFQ3tfpcZaP/54l4qGo5y59ULdZHSZ+kpyfzFeTPYsK+eze9q/KGRRkHQB+ccX3n8bX6/q4Zvf/RUrl9eQqo3123J2Cz+7Ozp5GSk8MsNB2jp6Pa52mAqq27lxy/s5crFxZx9SqHf5Qhw45lTKchK5a5ndulawQijIOjDmrcqeeLNQ/z1xXO48cypH1ifnZ7CJ1dMo72rh0feOEhYP/TDKhx2/P0T75CZlsw/XLXQ73LEk52ewpcvnccb++pZ81al3+XICVAQ9NLY3sW3ntrOkpJ8brtwVr/bTcrPZNWSyeyva9NMTcPs0Y0HeWN/PV+7fL7GE0ownzijhMVT8vj2b3fobHkEURD08s9P76ShvZvvfPTUAdudl03NZ9b4HJ7bfoTqlo5hqjDYDjUe5TtP7+BDM8dy/fIpfpcjvSQnGd9ctYia1k7uXr/H73IkRgqCKBv31/PoxoP8xbkzWDApd8DtzYyrl0yiO+T4ztqdw1BhsIXDji/9aivhsOOuaxdjpgvEiWhpST43nFHCfX/Yx6tlup10JFAQeEJhx51rSpmUl8EXL54d836FY9I5f3Yh//vmIV7bWzeEFcrP/lDO6+X1/MPVC5k2LtvvcuQ4vnbFAmYUZvN/HnmTw01H/S5HBqAg8Dy++SCllc3cfvl8stJOrGPSh+dOoGRsJl//9TZN0jFEth1q4rvrdrFyYRHXn64moUSXk57CTz51Oh3dIT73iy3qhJngFARAS0c33123i+XTCrhqcfEJ75+anMQ3rl5IWXUrP/tD+RBUGGx1rZ3c+tBmCnPSOX1aAY+8cZCHNxx435cknlkTxvDd65ew9WAjn//FFjq6FQaJSkEA/OiFMmpbu7jjqgWDbne+cN5ELl04kbvX71FHszjqDoX53C+3UNvayb2fWk62hpEYUS4/tZh/vGYR63dW8+mfb6Sts8fvkqQPgQ+C3VUt3PfyPq47fQqLp+Sf1GvdcdVCDOMbv9kep+qCzTnHHb/exhv76rnr2sWcOiXP75JkEJLMuP70Kby2t46Lv/8i339ut87iEkyggyAcdnz1iXcYk5HC318+/6Rfb3J+Jl+8eDbPba/id9s1j+vJcM7xzae288gbB/n8h0/hGk02M6Itm1rAp86aRvPRbu55oYyXdteoqSiBBPo8+5GNB9j8bgPfu34JY7PT4vKanz5nBv+zuYI7f1PKObMKY5oyUd7POcd3nt7JA6/s59PnzODLl871uyQZQCyf8OcV5fLFi+fw5JuHeKb0COf9ywt8+pwZfHz5FMblvL9jYH+vt3rFB3v6y8kLbBAcbjrKPz+9k7NmjuPa0+L3aTMtJYlvXbOIG+59nR+9sIcvXzovbq8dBB3dIb7yP2/z662V3HzWNL5+5Xz1FxhFctJTuGnFVPbWtLGnuoW7ntnJv6zbyWlTCzhvdiHzi3OZO3EMobDTQILDKJBB0NkT4rO/2EI47Pinj50a9z80H5o5jo8tm8y9L5Vz2aJiFk1W23YsKhra+ewvNlN6qJlLFkxkzsQxPPKGpp0cbcyMWRNyuOOqBew43My60iOs31HNv/3ujz2RDRiTkUJeZir5WWnev6mMzU5jcn4mk/IzGJudpg8JcRK4IHDOcceTpbx1sJH//ORpzCgcmo5Jf3/FfF4vr+OWBzfy5BfOoTgvc0jeZzQIhR0Pvrqf7z27iyQzPvmhacwvHrhnt4x884tzmV+cy/+9eA5tnT3srmphT1Urz5Qeoam9m8ajXRxuOsqOw830hB1PvX34vX3HpKewbFoBZ0wr4IK54zl1cp6CYZACFwT3/WEfj206yG1/MouVi068z0CsCnPSuf/Pz+C6/3iNP39gI49/7mzNoNVLOOx4bkcVd6/fQ2llMx+eO55vrVrEy5rlKpCy01NYNrWAZVML6Am/f0Rf5xxtXSHOPmUchxqPcrjxKLurW9m8v4F/fW43//rcbnIzUphXnMv8olxmjs9+b+h4XVcYWGD+MoXDjrvW7eQnL5Zz6cKJ/PVH5gz5e84ryuWem07j0z/fyCd/toH//OTpFOVlDPn7Jrq61k6eevswv9zwLrurWpk6Nosf3riMKxcX6xOd9MnMyElPYdHkvA80tda3dfHCzmruf2UfWw808sa+etKSk5gzMYcFk3K5YnExeZmpPlU+MsQUBGa2Evh3IBn4mXPun3utTwf+CzgdqAM+4Zzb7637KnALEAL+yjm3Lm7Vx6i6uYM7f1PK2neO8MkPTeXOq4ZvRqsL5oznntXL+JtfvcWVP3yZu29clvATqcT7jo2O7hA7j7Tw6t5aXi2r47XyOkJhx4LiXP7tE0u5cnExKcmBvpNZYnS8u5NuWjGN7lCY8po2dhxuZsfhZrZVNvPElkOcdco4Lpo3gbNOKWTOxBx94OhlwCAws2TgHuAjQAWw0czWOOeie03dAjQ452aZ2Q3AXcAnzGwBcAOwEJgE/M7M5jjnhuUG4urmDn6x4QA/famc7lCY2y+bx63nzxz2H4KVi4qZNSGHzzy0mdU/3cCfzB3P5/9kFsunFfj2AxkOO1o6e2g+2k2j1xbb5D1+payWUNgRco5Q2BEOO8A43HSU5CQjJclISU6K/JtkJHuPkwzaOkO0dfZQ09pSpdxRAAAMhElEQVRJZWMH79a1UV7bRsg71Z87cQx/ed5MPrpsMnOLxvjyvcvolZqcxNyiMcwtGsPVSydR0RAZ8O7Z0iPc6XX0HJedxuIpkTOL2RPHMHVsFpPzM8nLTCUtJZgfSGygKeXM7CzgTufcpd7zrwI4574Ttc06b5vXzCwFOAKMB26P3jZ6u/7eb/ny5W7Tpk0n/I0c7Qqx/XATe2va2FvTymt763i7IjLB/BWLi/nyJXOZfpIXho/3aSSWT8utnT38/JV93P/KfurbuijOy+CsmeNYOjWfkoIsivMzyE5LIT01iYzUZDJSkklNNsIuckE17P1h7vH+OHeHw7R3hmjt7KGts4e2rh5avT/EbZ09NB3tZuP+etq7QnR0h2jvCnG0K8TR7si/scyrZkTGmHdE2mnDMU7GlpeZSnFeBgYU5WVQlJfJ9HFZjMnQKbr4o76ti/KaVvbXtVHZ2EFNa+d7H1COyUhNivyMusit4CnJ3gefpCSSk4xTJuSQnpJEWkoSaclJpKdEflez0pLJTEsmMzXyb1ZaMpmpKVGP/7g8yYwkM8wiva4hMpRKdyj8XhPYYJjZZufc8sHsG8s7Tgai7+GrAFb0t41zrsfMmoBx3vLXe+07JF1Etx9u5tr/iORLarKxaHIeX750LpcunMisCYnxyTMnPYXbLpzNLefO5NdbD/Hynlpe3F3DE28eGpL3S7LIpOLRP6Rjs9Pe+8E8Z1bh+27Ny89MJS8zld++fZjkJCMpyd77QYVI2IW9IAp5QRQKRZ73hMOEHWSnJZOVlvLeJysNJSCJYmx2GmOzx7J8+lgAPnbaZA7Ut/NuXTuVjUdpPtr93lnytspmunpC9IQc3SFHR3cPPeEwRw+F6OoJ09kTpqsnRKf3OF6uWjKJH964LG6vF6tYgqCvtovenwv72yaWfTGzzwCf8Z62mtmuGOo6rjLgyZN9kQ8qBPq8peWm+L/Xyei3zmg/P8EXHYLvMaY6E8RIqVV1xtew1vkj4EerB7VrITBtsO8bSxBUACVRz6cAvWemPrZNhdc0lAfUx7gvzrl7gXtjL9sfZrZpsKdew0l1xt9IqVV1xtcIq3P6YPeP5crIRmC2mc0wszQiF3/X9NpmDXCz9/g64HkXufiwBrjBzNLNbAYwG3hjsMWKiEj8DXhG4LX53wasI3L76P3OuVIz+yawyTm3BrgPeMjMyoicCdzg7VtqZr8CtgM9wBeG644hERGJTUyXp51za4G1vZbdEfW4A7i+n32/DXz7JGpMJAnffOVRnfE3UmpVnfEViDoHvH1URERGt2D2nhARkfcoCGJkZivNbJeZlZnZ7X7Xc4yZlZjZC2a2w8xKzeyL3vKxZvacme3x/i3wu1aI9FQ3szfN7Cnv+Qwz2+DV+Zh3Q4LfNeab2eNmttM7rmcl4vE0s7/2/s+3mdkjZpaRKMfTzO43s2oz2xa1rM9jaBF3e79bb5vZaT7X+V3v//5tM/tfM8uPWvdVr85dZnapn3VGrftbM3NmVug9P+HjqSCIQdQwG5cBC4AbveEzEkEP8CXn3HzgQ8AXvNpuB9Y752YD673nieCLwI6o53cBP/DqbCAyXInf/h14xjk3D1hCpN6EOp5mNhn4K2C5c24RkRs5jg3vkgjH8+fAyl7L+juGlxG5o3A2kf5E/zFMNULfdT4HLHLOLQZ2A18F6DVkzkrgx97fBr/qxMxKiAz/E91z84SPp4IgNmcCZc65cudcF/AosMrnmgBwzh12zm3xHrcQ+aM1mUh9D3qbPQhc40+Ff2RmU4ArgJ95zw24EHjc28T3Os0sFzifyJ1wOOe6nHONJODxJHKzR6bXdycLOEyCHE/n3EtE7iCM1t8xXAX8l4t4Hcg3s6EbI36AOp1zzzrnerynrxPp/3Sszkedc53OuX1E+q2e6Vednh8Af8f7O+qe8PFUEMSmr2E2Em42dTObDiwDNgATnXOHIRIWwAT/KnvPvxH5oT3WJ38c0Bj1S5cIx3UmUAM84DVh/czMskmw4+mcOwR8j8gnwcNAE7CZxDue0fo7hon8+/Vp4GnvcULVaWZXA4ecc2/1WnXCdSoIYhPTUBl+MrMc4H+A/+uca/a7nt7M7Eqg2jm3OXpxH5v6fVxTgNOA/3DOLQPaSJxmtfd47eurgBlERvbNJtIk0JvfxzMWifhzgJl9jUjT6y+PLepjM1/qNLMs4GvAHX2t7mPZcetUEMQmpqEy/GJmqURC4JfOuSe8xVXHTge9f6v9qs9zDnC1me0n0rR2IZEzhHyvaQMS47hWABXOuQ3e88eJBEOiHc+LgX3OuRrnXDfwBHA2iXc8o/V3DBPu98vMbgauBG5yf7zHPpHqPIXIh4C3vN+pKcAWMytiEHUqCGITyzAbvvDa2e8Ddjjnvh+1KnrYj5uBXw93bdGcc191zk3xxkO5gcgwJDcBLxAZlgQSo84jwEEzm+stuohIz/iEOp5EmoQ+ZGZZ3s/AsToT6nj20t8xXAP8qXe3y4eApmNNSH6wyERcXwGuds61R61KmCFznHPvOOcmOOeme79TFcBp3s/viR9P55y+YvgCLidyB8Fe4Gt+1xNV17lETvveBrZ6X5cTaX9fD+zx/h3rd61RNX8YeMp7PJPIL1MZ8N9AegLUtxTY5B3TJ4GCRDyewDeAncA24CEgPVGOJ/AIkWsX3d4fqVv6O4ZEmjLu8X633iFyJ5SfdZYRaWM/9vv0n1Hbf82rcxdwmZ919lq/Hygc7PFUz2IRkYBT05CISMApCEREAk5BICIScAoCEZGAUxCIiAScgkBEJOAUBJKQzOznZvaPQ/C6d5rZL+L9uiIjmYJAfGNm+83sqJm1mlmDmf3WG1bX77qme+O7t3pf+y2B5qAQiTcFgfjtKudcDlAMVAE/9LmeaPlebTcCd3hDD7xP1Lg+vkmEGmRkUxBIQnDOdRAZ4K3PCX/M7C+9GZfqzWyNmU2KWne2mW00sybv37Oj1s0wsxfNrMXMngMKB1Hba0ApsMh7TWdmXzCzPUSGS8DM5llk1q16b/aqj0fVcLmZbfdqOGRmf+stLzSzp8ys0dvvZTNLinqPWVGv8V5TmZl92MwqzOwrZnYEeMBbfqWZbfVe71UzW3yi36sEk4JAEoI3rO4niEwE0nvdhcB3gI8TOXN4l8gIppjZWOC3wN1ExrL5PvBbMxvn7f4wkXH6C4Fv8cdBz2Kty8zsHCKzUr0ZteoaYAWwwJuv4DnvvSYQOYP4sZkt9La9D7jVOTeGSJg87y3/EpFxY8YDE4G/J/ZhjYuAscA04DMWmY7wfuBWIsfhJ8AaM0s/ke9XgklBIH570swagWYiU+59t49tbgLud85tcc51Epk68CxvIp4rgD3OuYeccz3OuUeIDMR2lZlNBc4Avu4is0q9BPzmBGqrJTIr1M+A251z66PWfcc5V++cO0pkuOL9zrkHvBq2EBkW/NgooN1EAiPXOdfgrT+2vBiY5pzrds697GIf/CsM/IP3fR0F/hL4iXNug3Mu5Jx7EOgkMn2pyHEpCMRv1zjn8omMnHkb8KI3pnq0SUTOAgBwzrUCdURmXXrfOs+7UesanHNtvdbFqtA5V+Ccm++cu7vXuugZoKYBK7wmmUYv2G4i8qkd4FoiI8K+6zVTneUt/y6RkS6fNbPyE7wgXeM1p0XX8KVeNZQQOQYix6UgkITgfYp9AggRGVo7WiWRP3QAeE0x44BDvdd5pnrrDgMF3vbR6+JSctTjg8CLzrn8qK8c59znAJxzG51zq4g0Gz0J/Mpb3uKc+5JzbiZwFfA3ZnaR95rtROYhPqZ3OPY+czgIfLtXDVneGZLIcSkIJCF4bfGriIz9v6PX6oeBPzezpV6b9z8BG5xz+4G1wBwzW21mKWb2CSIXnJ9yzr1LZF6Bb5hZmpmdS+QPbrw95dXwKTNL9b7OMLP53vveZGZ5LjKTWDORsDt2cXeWmVnU8pD3mluB1WaW7N2tdMEANfwU+KyZrfCOZbaZXWFmY4bg+5VRRkEgfvuNmbUS+UP4beBm51xp9AZe2/zXibS7HyYyTd8N3ro6Im30XyLSXPR3wJXOuVpv99VELurWA/8A/Fe8vwHnXAtwiVdTJXAEuItIcxfAp4D9ZtYMfBb4pLd8NvA7oBV4Dfixc+733rovEgmtY81MTw5QwyYi1wl+BDQQaXL6s5P+5iQQNDGNiEjA6YxARCTgFAQSSF67fWsfX6UD7y0yuqhpSEQk4HRGICIScAoCEZGAUxCIiAScgkBEJOAUBCIiAff/AWdizmwcST45AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#舒张压blood_pressure直方图\n",
    "x_blood_pressure=plt.figure()\n",
    "sns.distplot(train['blood_pressure'], bins=40 ,kde=True)\n",
    "plt.xlabel(\"Blood_Pressure\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上直方图看出：大部分数据集中在舒张压60至80之间，而在0处，可以认为是噪声点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三头肌皮褶厚度Triceps_skin_fold_thickness直方图\n",
    "x_triceps_skin_fold_thickness=plt.figure()\n",
    "sns.distplot(train['Triceps_skin_fold_thickness'], bins=40 ,kde=True)\n",
    "plt.xlabel(\"Triceps_Skin_Fold_Thickness\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#餐后血清胰岛素serum_insulin直方图\n",
    "x_serum_insulin=plt.figure()\n",
    "sns.distplot(train['serum_insulin'], bins=40 ,kde=True)\n",
    "plt.xlabel(\"Serum_Insulin\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#体重指数BMI直方图\n",
    "x_bmi=plt.figure()\n",
    "sns.distplot(train['BMI'], bins=40, kde=True)\n",
    "plt.xlabel(\"BMI\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上直方图看出：大部分数据集中在体重指数30至40之间，而在0处，可以认为是噪声点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#糖尿病家系作用Diabetes_pedigree_function直方图\n",
    "x_diabetes_pedigree_function=plt.figure()\n",
    "sns.distplot(train['Diabetes_pedigree_function'], bins=40, kde=True)\n",
    "plt.xlabel(\"Diabetes_Pedigree_Function\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上直方图看出：大部分数据集中在0.4附近，而1.5以后的可以认为忽略不计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年龄Age直方图\n",
    "x_pregnants=plt.figure()\n",
    "sns.countplot(train['Age'])\n",
    "plt.xlabel(\"Age\", fontsize=12)\n",
    "plt.ylabel(\"Number Of Age\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上直方图看出：大部分数据集中在中年，对于老年区间的数据比较少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "考虑两两特征之间的关系\n",
    "1、数值特征--数值特征\n",
    "1.1 相关矩阵：两个数值特征之间的关系可用相关矩阵来查看它们之间的相关性。因为有些机器学习算法（如普通最小二乘线性回归）不能很好地处理高度相关\n",
    "的输入变量。如果特征之前高度相关，可考虑进行PCA降维（特征层面）或者加正则项（模型层面）。\n",
    "可用DataFrame的corr()方法先计算出每对特征间的相关矩阵，然后将所得的相关矩阵传给seaborn的heatmap()方法，渲染出一个基于色彩编码的矩阵。\n",
    "1.2散点图(scatter)将两个数值变量的值显示为二维空间中的笛卡尔坐标。\n",
    "\n",
    "2、数值特征--类型特征\n",
    "散点图中的数据点可以通过色彩或者尺寸进行编码，以便在同一张图像中包括第三个类别变量的值。\n",
    "也可以用lmplot()函数的hue参数来指定感兴趣的类别特征。\n",
    "violinplot/boxplot可表示输入两个特征，用于表示在一个特征取值下(类型型特征)，另一个特征(数值特征)的分布或统计量。\n",
    "\n",
    "3、类别特征--类别特征\n",
    "通过设置参数hue，在图像中加入类别维度\n",
    "除了使用图形进行类别分析之外，还可以使用统计学的传统工具：列联表(contingency table)，又称为交叉制表(cross tabulation)，使用表格形式表示多个\n",
    "类别变量的频率分布。我们可以通过查看一列或者一行来得知某个变量在另一个变量的作用下的分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbff2e80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关矩阵(此处没有考虑相关系数的正负)\n",
    "cols=train.columns#得到每一列\n",
    "\n",
    "#计算每两个特征之间的相关系数\n",
    "data_corr=train.corr()\n",
    "#打印热力图\n",
    "sns.heatmap(data_corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将上面得到的相关系数取绝对值。一般认为相关系数大于0.5的值，为强相关\n",
    "data_corr=data_corr.abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 12))#设置热力图大小\n",
    "sns.heatmap(data_corr, annot=True)\n",
    "\n",
    "sns.heatmap(data_corr, mask=data_corr<0.5, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape#获取相关矩阵的大小。输出结果为15*15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants and Age = 0.54\n"
     ]
    }
   ],
   "source": [
    "#筛选强相关系数\n",
    "#初始化阈值\n",
    "threshold=0.5\n",
    "\n",
    "corr_list=[]#定义列表\n",
    "size=data_corr.shape[0]#获取相关矩阵的大小\n",
    "\n",
    "#循环相关矩阵，筛选强相关系数\n",
    "for i in range(0, size):\n",
    "    for j in range(i+1, size):\n",
    "        if(data_corr.iloc[i, j] >= threshold and data_corr.iloc[i, j]<1) or (data_corr.iloc[i, j]<0 and data_corr.iloc[i, j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i, j], i, j])#相关系数加入列表\n",
    "            \n",
    "#相关系数排序\n",
    "s_corr_list=sorted(corr_list, key=lambda x:-abs(x[0]))\n",
    "\n",
    "#显示强相关系数对应的两两特征\n",
    "for v,i,j in s_corr_list:\n",
    "    print(\"%s and %s = %.2f\" % (cols[i], cols[j], v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由以上数据看出：怀孕次数与年龄有相关性"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
